US2025193090A1PendingUtilityA1
Quality of experience for telecommunications networks
Est. expiryDec 7, 2043(~17.4 yrs left)· nominal 20-yr term from priority
H04L 43/08H04L 43/0888H04L 41/5067H04W 24/02H04L 41/16
39
PatentIndex Score
0
Cited by
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References
0
Claims
Abstract
A method for training a machine learning model to generate a quality of experience (QOE) score that may include providing a machine learning module with a data set comprising QOE data, wherein the QOE data comprises a set of pairings or a communication timing statistic and an associated human user feedback QOE score for the communication timing statistic. The machine learning module may be trained on the QOE data to generate QOE scores for each of the pairings of communication timing statistics and human user feedback QOE scores to minimize error, where error may be the aggregated difference of generated QOE scores and the human user feedback QOE scores. Also, a method for calculating a QOE score from a communication timing statistic may include generating a QOE score considering a DNS component, a TCP component, a TLS component, a latency component, and a data rate component.
Claims
exact text as granted — not AI-modifiedWe claim:
1 . A method for training a machine learning model to generate a quality of experience (QOE) score comprising:
providing a machine learning module with a data set comprising QOE data, wherein the QOE data comprises a set of pairings or a communication timing statistic and an associated human user feedback QOE score for the communication timing statistic; training the machine learning module on the QOE data to generate QOE scores for each of the pairings of communication timing statistics and human user feedback QOE scores to minimize error, where error comprises the aggregated difference of generated QOE scores and the human user feedback QOE scores.
2 . The method of claim 1 wherein the communication timing statistic in the QOE data comprise curl probe statistics.
3 . The method of claim 1 wherein the curl probe statistics comprise a DNS time, a TCP time, a TTFB, a total time and data size.
4 . The method of claim 3 wherein the training the machine learning module to generate QOE scores for each of the pairings of communication timing statistics and human user feedback QOE Scores to minimize error comprises using the DNS time, the TCP Time, the TLS time, effective latency and effective data rate, wherein effective latency comprises the difference between the TTFB time and the TLS time, and effective data rate comprises the data size divided by the difference between total time and TLS time.
5 . The method of claim 4 wherein the training the machine learning module to generate QOE scores for each of the pairings of communication timing statistics and human user feedback QOE scores to minimize error comprises generating a weight to be given to DNS time (Wgt_DNS).
6 . The method of claim 4 wherein the training the machine learning module to generate QOE scores for each of the pairings of communication timing statistics and human user feedback QOE scores to minimize error comprises generating a weight to be given to TCP time (Wgt_TCP).
7 . The method of claim 4 wherein the training the machine learning module to generate QOE scores for each of the pairings of communication timing statistics and human user feedback QOE scores to minimize error comprises generating a weight to be given to TLS time (Wgt_TLS).
8 . The method of claim 4 wherein the training the machine learning module to generate QOE scores for each of the pairings of communication timing statistics and human user feedback QOE Scores comprises generating a weight to be given to effective latency (Wgt_Latency).
9 . The method of claim 4 wherein the training the machine learning module to generate QOE scores for each of the pairings of communication timing statistics and human user feedback QOE Scores comprises generating a weight to be given to effective data rate (Wgt_DR).
10 . The method of claim 4 wherein the training the machine learning module to generate QOE scores for each of the pairings of communication timing statistics and human user feedback QOE scores to minimize error comprises:
generating a weight to be given to DNS time (Wgt_DNS);
generating a weight to be given to TCP time (Wgt_TCP);
generating a weight to be given to TLS time (Wgt_TLS);
generating a weight to be given to effective latency (Wgt_latency);
generating a weight to be given to effective data rate (Wgt_DR).
11 . The method of claim 10 wherein the weights for DNS time, TCP time, TLS time, effective latency and data rate are normalized to sum to 1.
12 . The method of claim 4 wherein the training the machine learning module to generate QOE scores for each of the pairings of communication timing statistics and human user feedback QOE scores to minimize error comprises generating thresholds for DNS time (Alpha_DNS), TCP time (Alpha_TCP), and TLS time (Alpha_TLS) below which the DNS time, TCP time and TLS time have no effect on QOE score.
13 . The method of claim 4 wherein the training the machine learning module to generate QOE scores for each of the pairings of communication timing statistics and human user feedback QOE scores to minimize error comprises generating a threshold rate above which effective data rate has no effect on QOE Score.
14 . The method of claim 4 wherein the training the machine learning module to generate QOE scores for each of the pairings of communication timing statistics and human user feedback QOE scores to minimize error comprises generating a DNS factor (Beta_DNS) for the effect of DNS time that is greater than a threshold DNS time (Alpha_DNS) on the QOE score.
15 . The method of claim 4 wherein the training the machine learning module to generate QOE scores for each of the pairings of communication timing statistics and human user feedback QOE scores to minimize error comprises generating a TCP factor (Beta_TCP) for the effect of TCP time that is greater than a threshold TCP time (Alpha_TCP) on the QOE score.
16 . The method of claim 4 wherein the training the machine learning module to generate QOE scores for each of the pairings of communication timing statistics and human user feedback QOE scores to minimize error comprises generating a TLS factor (Beta_TLS) for the effect of TLS time that is greater than a threshold TLS time (Alpha_TLS) on the QOE score.
17 . The method of claim 4 wherein the training the machine learning module to generate QOE scores for each of the pairings of communication timing statistics and human user feedback QOE scores to minimize error comprises generating a latency factor (Beta_Latency) for the effect of effective latency on the QOE score.
18 . The method of claim 4 wherein the training the machine learning module to generate QOE scores for each of the pairings of communication timing statistics and human user feedback QOE scores to minimize error comprises generating a data rate factor (Beta_DR) for the effect of effective data rate below a threshold data rate (Alpha_DR) on the QOE score.
19 . A method for generating a quality of experience score from a curl probe statistic comprising:
QOE
Score
=
max
(
0
,
Wgt_DNS
⋆
(
max
(
0
,
DNS_time
_ms
-
Alpha_DNS
)
⋆
(
Beta_DNS
)
+
1
0
0
)
+
Wgt_TCP
⋆
(
max
(
0
,
TCP_time
_ms
-
Alpha_TCP
)
⋆
(
Beta_TCP
)
+
1
0
0
)
+
Wgt_TLS
⋆
(
max
(
0
,
TLS_time
_ms
-
Alpha_TLS
)
⋆
(
Beta_TLS
)
+
1
0
0
)
+
Wgt_Latency
⋆
(
Latency_time
_ms
⋆
(
Beta_Latency
)
+
100
)
+
Wgt_DR
⋆
(
76.92
-
461.53
⋆
1.01
⋀
(
Beta_DR
⋆
min
(
Effective_data
_rate
_kbps
,
Alpha_DR
)
)
,
wherein the curl probe statistic comprises DNS_time_ms (DNS time in milliseconds), TCP_time_ms (TCP time in milliseconds), TLS_time_ms (TLS time in milliseconds), TTFB_time_ms (TTFB time in milliseconds), total_time_ms (total time in milliseconds) and data_size;
wherein Latency_time_ms comprises the difference between the TTFB_time_ms and TLS_time_ms;
wherein Effective_data_rate_kbps comprises the data_size divided by the difference between total_time_ms and tls_time_ms expressed as kilobits per second;
wherein Wgt_DNS is a weight to be assigned to DNS time, Wgt_TCP is a weight to be assigned to TCP time, Wgt_TLS is a weight to be assigned to effective latency, Wgt_DR is a weight to be assigned to effective data range, Alpha_DNS is a threshold DNS time below which DNS_time_ms is irrelevant, Alpha_TCP is a threshold TCP time below which TCP_time_ms is irrelevant, Alpha_TLS is a threshold TLS time below which TLS_time_ms is irrelevant, Alpha_DR is a threshold data rate above which Effective_data_rate_kbps is irrelevant, Beta_DNS is a factor for the rate at which DNS_time_ms above Alpha DNS affects the QOE Score, Beta_TCP is a factor for the rate at which TCP_time_ms above Alpha_TCP affects the QOE Score, Beta_TLS is a factor for the rate at which TLS_time_ms above Alpha_TLS affects the QOE Score, Beta_DR is a factor for the rate at which Effective_data_rate_kbps below Alpha_DR affects the QOE Score.
20 . A method for generating a quality of experience score from a curl probe statistic comprising:
QOE
score
=
max
(
0
,
0.1
*
(
max
(
0
,
DNS_time
_ms
-
5
0
0
)
*
(
-
0
.
0
7
1
3
)
+
1
0
0
)
+
0
.
1
5
*
(
max
(
0
,
TCP_time
_ms
-
5
00
)
⋆
(
-
0
.
0
4
2
5
)
+
1
0
0
)
+
0.25
(
max
(
0
,
TLS_time
_ms
-
5
00
)
⋆
(
-
0
.
0
4
)
+
1
0
0
)
+
0.37
⋆
(
Latency_time
_ms
⋆
(
-
0
.
0
2
)
+
1
0
0
)
+
0.13
⋆
(
7
6
.92
-
461.53
⋆
1.01
⋀
(
-
0.78
⋆
min
(
Effective_data
_rate
_kbps
,
500
)
)
wherein the curl probe statistic comprises DNS_time_ms (DNS time in milliseconds), TCP_time_ms (TCP time in milliseconds), TLS_time_ms (TLS time in milliseconds), TTFB_time_ms (TTFB time in milliseconds), total_time_ms (total time in milliseconds) and data_size;
wherein Latency_time_ms comprises the difference between the TTFB_time_ms and TLS_time_ms; and
wherein Effective_data_rate_kbps comprises the data_size divided by the difference between total_time_ms and tls_time_ms expressed as kilobits per second.Join the waitlist — get patent alerts
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